Predicting the Tumour Response to Radiation by Modelling the Five Rs of Radiotherapy Using PET Images
Despite the intensive use of radiotherapy in clinical practice, its effectiveness depends on several factors. Several studies showed that the tumour response to radiation differs from one patient to another. The non-uniform response of the tumour is mainly caused by multiple interactions between the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/9/6/124 |
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| author | Rihab Hami Sena Apeke Pascal Redou Laurent Gaubert Ludwig J. Dubois Philippe Lambin Dimitris Visvikis Nicolas Boussion |
| author_facet | Rihab Hami Sena Apeke Pascal Redou Laurent Gaubert Ludwig J. Dubois Philippe Lambin Dimitris Visvikis Nicolas Boussion |
| author_sort | Rihab Hami |
| collection | DOAJ |
| description | Despite the intensive use of radiotherapy in clinical practice, its effectiveness depends on several factors. Several studies showed that the tumour response to radiation differs from one patient to another. The non-uniform response of the tumour is mainly caused by multiple interactions between the tumour microenvironment and healthy cells. To understand these interactions, five major biologic concepts called the “5 Rs” have emerged. These concepts include reoxygenation, DNA damage repair, cell cycle redistribution, cellular radiosensitivity and cellular repopulation. In this study, we used a multi-scale model, which included the five Rs of radiotherapy, to predict the effects of radiation on tumour growth. In this model, the oxygen level was varied in both time and space. When radiotherapy was given, the sensitivity of cells depending on their location in the cell cycle was taken in account. This model also considered the repair of cells by giving a different probability of survival after radiation for tumour and normal cells. Here, we developed four fractionation protocol schemes. We used simulated and positron emission tomography (PET) imaging with the hypoxia tracer 18F-flortanidazole (18F-HX4) images as input data of our model. In addition, tumour control probability curves were simulated. The result showed the evolution of tumours and normal cells. The increase in the cell number after radiation was seen in both normal and malignant cells, which proves that repopulation was included in this model. The proposed model predicts the tumour response to radiation and forms the basis for a more patient-specific clinical tool where related biological data will be included. |
| first_indexed | 2024-03-11T02:17:12Z |
| format | Article |
| id | doaj.art-432d5eb67ea043b1a19294fa5981dbe9 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-11T02:17:12Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-432d5eb67ea043b1a19294fa5981dbe92023-11-18T11:04:32ZengMDPI AGJournal of Imaging2313-433X2023-06-019612410.3390/jimaging9060124Predicting the Tumour Response to Radiation by Modelling the Five Rs of Radiotherapy Using PET ImagesRihab Hami0Sena Apeke1Pascal Redou2Laurent Gaubert3Ludwig J. Dubois4Philippe Lambin5Dimitris Visvikis6Nicolas Boussion7INSERM UMR 1101 “LaTIM”, CEDEX 3, 29238 Brest, FranceINSERM UMR 1101 “LaTIM”, CEDEX 3, 29238 Brest, FranceINSERM UMR 1101 “LaTIM”, CEDEX 3, 29238 Brest, FranceINSERM UMR 1101 “LaTIM”, CEDEX 3, 29238 Brest, FranceThe D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6211 LK Maastricht, The NetherlandsThe D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6211 LK Maastricht, The NetherlandsINSERM UMR 1101 “LaTIM”, CEDEX 3, 29238 Brest, FranceINSERM UMR 1101 “LaTIM”, CEDEX 3, 29238 Brest, FranceDespite the intensive use of radiotherapy in clinical practice, its effectiveness depends on several factors. Several studies showed that the tumour response to radiation differs from one patient to another. The non-uniform response of the tumour is mainly caused by multiple interactions between the tumour microenvironment and healthy cells. To understand these interactions, five major biologic concepts called the “5 Rs” have emerged. These concepts include reoxygenation, DNA damage repair, cell cycle redistribution, cellular radiosensitivity and cellular repopulation. In this study, we used a multi-scale model, which included the five Rs of radiotherapy, to predict the effects of radiation on tumour growth. In this model, the oxygen level was varied in both time and space. When radiotherapy was given, the sensitivity of cells depending on their location in the cell cycle was taken in account. This model also considered the repair of cells by giving a different probability of survival after radiation for tumour and normal cells. Here, we developed four fractionation protocol schemes. We used simulated and positron emission tomography (PET) imaging with the hypoxia tracer 18F-flortanidazole (18F-HX4) images as input data of our model. In addition, tumour control probability curves were simulated. The result showed the evolution of tumours and normal cells. The increase in the cell number after radiation was seen in both normal and malignant cells, which proves that repopulation was included in this model. The proposed model predicts the tumour response to radiation and forms the basis for a more patient-specific clinical tool where related biological data will be included.https://www.mdpi.com/2313-433X/9/6/124radiotherapyfive Rs of radiobiologytumour responsesimulationPET images |
| spellingShingle | Rihab Hami Sena Apeke Pascal Redou Laurent Gaubert Ludwig J. Dubois Philippe Lambin Dimitris Visvikis Nicolas Boussion Predicting the Tumour Response to Radiation by Modelling the Five Rs of Radiotherapy Using PET Images Journal of Imaging radiotherapy five Rs of radiobiology tumour response simulation PET images |
| title | Predicting the Tumour Response to Radiation by Modelling the Five Rs of Radiotherapy Using PET Images |
| title_full | Predicting the Tumour Response to Radiation by Modelling the Five Rs of Radiotherapy Using PET Images |
| title_fullStr | Predicting the Tumour Response to Radiation by Modelling the Five Rs of Radiotherapy Using PET Images |
| title_full_unstemmed | Predicting the Tumour Response to Radiation by Modelling the Five Rs of Radiotherapy Using PET Images |
| title_short | Predicting the Tumour Response to Radiation by Modelling the Five Rs of Radiotherapy Using PET Images |
| title_sort | predicting the tumour response to radiation by modelling the five rs of radiotherapy using pet images |
| topic | radiotherapy five Rs of radiobiology tumour response simulation PET images |
| url | https://www.mdpi.com/2313-433X/9/6/124 |
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